Page 25 Science, Technology and Innovation for Enhanced Growth

Evaluation of Self-Modifying Cellular Automata In Modelling Urban
Growth In Nyeri (Kenya)
Kenneth Mubea I and Gunter Menz2
J Institute
of Geomatics, GIS and Remote Sensing (IGGReS),
Dedan Kimathi University of Technology, P. O.Box. 657 ~ 10100, Nyeri, KENYA.
2Remote Sensing Research Group (RSRG)
University of Bonn, Meckenheimer/sllee 166,53115 Bonn, Germany.
Corresponding Author Email: mubea@uni-bonn.de
Abstract
Urban growth modelling using cellular automata has blossomed due to the
advancement in geographic information systems (GIS), remote sensing and computer
technology.
Among such urban growth models, our urban growth model (UGM), was
modified from SLEUTH (Slope Land-use Transport Hill-shade) model. UGM has been
integratedin the XULU modelling frame-work (eXtendable Unified Land Use Modelling
Platform). In this research we evaluated a modified UGM whose transition rules were
modified. In order to arrive at urban growth modelling, we used multi-temporal Landsat
satellite image sets for 1987 and 2010 to map urban land-use in Nyeri.We compared our
results with a normal UGM simulation. Thus, we arrived at two urban growth simulations for
Science, Technology and Innovation for Enhanced Growth
Page 25
---
~----
Differentiation of Some Crops in Leingarten and Moessingen, BadenWuerttemberg, Germany Using TerraSAR-X Data
Kuria B. Thiong'oa* Prof. Alfred Kleusbergb* Dipl. -Geogr. Renee' Pastemakc*
aIGGReS, Dedan Kimathi University of Technology
P.O. Box 384-00208 Ngong Hills tbk
. @ J
UYla va lOa.com
+49152/11983321
blnstitute of Navigation, Stuttgart University
Breitscheidstr. 2 D-70174 Stuttgart
kleusberg@nav.uni-stuttgart.de
+49 711 /685-83400
"Institue of Navigation, Stuttgart University
Breitscheidstr. 2 D-70174 Stuttgart
pasternak@nav.uni-stuttgart.de
.,,-....
+49 711 /685-84719
Abstract
The aim of this research was to try and differentiate some of the important crop cultures in
the Baden-Wuertternberg area by creating crop signatures from the backscatter values of
the different crops from the different test sites. At the end it was also to establish whether
the signatures of one crop (e.g. Maize) collected in one of the test site can be compared or
transferred to another test site. This study was done using the TerraSAR-X data, with VV
polarization, which was overlaid with crop fields' ground truth data that was collected from
the fields. Images covering two test sites at Leingarten and Moessingen for the months of
July and August were used. These test sites were located in different climatic regions that
had different sowing times, crop development times and harvesting times as was elaborate
in the "Klimaatlas maps". Only radiometric correction was carried out on the images. The
speckle noise was not removed or minimized. The crops were classified according to the
mean and standard deviation of their backscatter. The results obtained were then
compared to results of other researches for checks and accuracies.
Keywords: TerrarSAR -X, radar backscatter, Crop signatures
Science, Technology and Innovation for Enhanced Growth
Page 24